Extracting common motifs under the levenshtein measure: Theory and experimentation

12Citations
Citations of this article
6Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Using our techniques for extracting approximate non-tandem repeats[1] on well constructed maximal models, we derive an algorithm to find common motifs of length P that occur in N sequences with at most D differences under the Edit distance metric. We compare the effectiveness of our algorithm with the more involved algorithm of Sagot[17] for Edit distance on some real sequences. Her method has not been implemented before for Edit distance but only for Hamming distance[12,20]. Our resulting method turns out to be simpler and more efficient theoretically and also in practice for moderately large P and D.

Cite

CITATION STYLE

APA

Adebiyi, E. F., & Kaufmann, M. (2002). Extracting common motifs under the levenshtein measure: Theory and experimentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2452, pp. 140–156). Springer Verlag. https://doi.org/10.1007/3-540-45784-4_11

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free